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Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops

Gang Zhao 1, * Holger Hoffmann 1 Jagadeesh Yeluripati 2, 3 Specka Xenia 4 Claas Nendel 5 Elsa Coucheney 3 Matthias Kuhnert 6 Fulu Tao 7 Julie Constantin 8 Helene Raynal 9 Edmar Teixeira 10 Balázs Grosz 11 Luca Doro 12, 13 Ralf Kiese 14 Henrik Eckersten 15 Edwin Haas 14 Davide Cammarano 16 Belay Kassie 16 Marco Moriondo 17 Giacomo Trombi 18 Marco Bindi 18 Christian Biernath 19 Florian Heinlein 19 Christian Klein 19 Eckart Priesack 19 Elisabet Lewan 3 Kurt-Christian Kersebaum 5 Reimund Rötter 7 Pier Paolo Roggero 12 Daniel Wallach 8 Senthold Asseng 16 Stefan Siebert 1 Thomas Gaiser 1 Frank Ewert 1
Abstract : We compared the precision of simple random sampling (SimRS) and seven types of stratified random sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen crop models. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modestly but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, especially when the sensitivity behavior of a crop model is known.
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Submitted on : Wednesday, May 27, 2020 - 9:50:22 PM
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Gang Zhao, Holger Hoffmann, Jagadeesh Yeluripati, Specka Xenia, Claas Nendel, et al.. Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops. Environmental Modelling and Software, Elsevier, 2016, 80, pp.100-112. ⟨10.1016/j.envsoft.2016.02.022⟩. ⟨hal-02636966⟩

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